Validation of a coding algorithm to identify patients with hepatocellular carcinoma in an administrative database

Authors

  • David S. Goldberg,

    Corresponding author
    1. Department of Medicine, Division of Gastroenterology, Hospital of the University of Pennsylvania, PA, USA
    • Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, PA, USA
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  • James D. Lewis,

    1. Department of Medicine, Division of Gastroenterology, Hospital of the University of Pennsylvania, PA, USA
    2. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, PA, USA
    3. Leonard Davis Institute of Health Economics, University of Pennsylvania, PA, USA
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  • Scott D. Halpern,

    1. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, PA, USA
    2. Leonard Davis Institute of Health Economics, University of Pennsylvania, PA, USA
    3. Department of Medicine, Division of Pulmonary, Allergy, and Critical Care, Hospital of the University of Pennsylvania, PA, USA
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  • Mark G. Weiner,

    1. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, PA, USA
    2. Leonard Davis Institute of Health Economics, University of Pennsylvania, PA, USA
    3. Department of Medicine, Division of General Internal Medicine, Hospital of the University of Pennsylvania, PA, USA
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  • Vincent Lo Re III

    1. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, PA, USA
    2. Department of Medicine, Division of Infectious Diseases, Hospital of the University of Pennsylvania, PA, USA
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David Goldberg, Hospital of the University of Pennsylvania, 3400 Spruce Street, 9 Penn Tower, Philadelphia, PA 19104, USA. E-mail: david.goldberg@uphs.upenn.edu

ABSTRACT

Purpose

International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM)-based algorithms to identify patients with hepatocellular carcinoma (HCC) have not been developed outside of the Veterans Affairs healthcare setting. The development and validation of such algorithms are necessary for the conduct of population-based studies evaluating the epidemiology and comparative effectiveness and safety of therapies for HCC.

Methods

We queried electronic medical records at two tertiary care hospitals to identify patients with two ICD-9-CM diagnosis codes for a chronic liver disease and/or cirrhosis plus two ICD-9-CM codes for HCC. We determined the positive predictive value (PPV) of this algorithm by comparing it to diagnoses of HCC confirmed by expert medical record review.

Results

Among 101 patients meeting the algorithm, 88 (PPV: 87.1%; 95% CI: 79.0–93.0%) had confirmed HCC. The algorithm's sensitivity was 91.7% among patients with confirmed HCC, and its specificity was 98.7% among chronic liver disease patients without HCC. Excluding patients who received systemic chemotherapy in the 12 months prior to or 6 months after the initial ICD-9-CM code in the algorithm, the PPV increased to 91.6% (87/95; 95% CI: 84.1–96.3%).

Conclusions

The presence of at least two ICD-9-CM codes for a chronic liver disease and/or cirrhosis plus two ICD-9-CM codes for HCC has a high PPV for identifying HCC cases. This simple, claims-based algorithm can be used in future epidemiologic studies to examine risk factors for HCC and evaluate outcomes and adverse events of medical therapies prescribed for HCC patients. Copyright © 2012 John Wiley & Sons, Ltd.

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